Your company grew. Your data didn't keep up.
I help mid-market companies that grew by acquisition get one reliable set of numbers across every site, system, and entity, so leadership can see the whole business clearly.
Forward-deployed data engineering: ingestion, warehouse, transformation, governance.
Architecture assessments, analytics foundation builds, and fractional data engineering leadership for mid-market operators running several ERPs and legacy systems after acquisitions.
Not ready to talk? Get the free guide to the five problems that keep analytics stuck, and how to fix them.
The problem
Every acquisition added another system.
Each company still runs its own ERP, with its own customers, item codes, and chart of accounts
Month-end and board reporting stitched together from exports and spreadsheets that break silently
No data team on staff, and six-figure consulting quotes to get one
Payroll, pricing, and margin data in shared files with no real access controls
Questions from leadership and investors that take weeks to answer, if they can be answered at all
Free guide
From trapped data to a foundation your team owns
The five problems that keep analytics stuck, what a sound data foundation includes, and what it takes to build one. Plain-language and technical views side by side.
The approach
Assess. Build. Enable.
Assess
A focused review of how your data flows today, what's broken or missing, and what it takes to fix it. You get a clear, plain-language plan, not a 200-page report.
A fixed-scope review of your source systems and entities, data volumes, security and access requirements, and team capabilities. You get a written target-state architecture, a tooling recommendation, and a phased build plan.
Build the Foundation
Automated pipelines bring every company's data into one secure, organized place, with shared definitions, so the numbers agree across the business. Typically 4 to 8 weeks.
I deploy an opinionated reference stack (ingestion, warehouse, transformation layer, governance) with reusable infrastructure as code. Typically 4 to 8 weeks.
Enable Your Team
Your analyst, finance team, or IT partner gets clean, organized data, clear documentation, and a walkthrough so they can build their own reports. No data team yet? I can stay on as your fractional lead.
Your team gets tested, documented data marts, a documented access model, a runbook, and a recorded enablement walkthrough. You own your analytics and never wait on a consultant for a dashboard.
What you get
A data foundation your team can build on
I build the infrastructure. Your team builds the insights.
Automated, reliable pipelines from every ERP and system you run, with no migration required
One consistent view across entities: shared customer, item, and account definitions
Access controls that keep payroll, pricing, and other sensitive data limited to the right people
A recorded walkthrough and runbook your team keeps
The ownership boundary
What I build vs. what stays yours
Delivery stops at clean, modeled data your team can query. A semantic layer is available as an Expansion add-on. Dashboards stay with your team by design. Hover or tap a layer to see what it does.
What we can do together
Engagement options
Start here
Data Strategy & Architecture Assessment
A 1 to 2 week engagement that maps where you are and where you need to go. You get a clear plan with honest recommendations. Low risk, high clarity.
A fixed-fee, 1 to 2 week engagement: a current-state review, a written target-state architecture, and a phased build plan. Low risk, high clarity.
The flagship
Analytics Foundation Build
I build the data foundation, walk your team through it, and hand you the keys. Your team builds its own reports on top. Fixed scope, fixed price.
Ingestion, warehouse, transformation layer, and governance, plus an enablement handoff so your team can build reporting on top. Fixed scope, fixed price.
Ongoing
Fractional Data Engineering Lead
Ongoing data leadership without a full-time hire. I watch your systems, fix problems during business hours, improve them as your needs change, and act as your team's go-to data expert.
I stay on as the accountable owner of your data platform: pipeline monitoring, business-hours issue resolution, schema drift, model evolution, and senior advisory, without a full-time hire.
Private equity operating partner? See how this works across a portfolio.
Proof
Background
Experience
8+ years building supply chain, inventory, and demand planning analytics, then the data and AI platforms under them: production Snowflake environments, multi-tenant data architectures, CDC replication platforms, and ML infrastructure, built to regulated-industry standards.
Credentials
M.S. Analytics (Data Science), Georgia Institute of Technology. B.S. Statistics & Applied Mathematics, Rose-Hulman Institute of Technology. Experience setting AI governance policy and responsible-AI review standards in a regulated environment.
About
Kennedy Schnieders
I've spent 8+ years at a multi-site medical device manufacturer, first as a data scientist solving supply chain, inventory, and demand planning problems, then as the person responsible for building the data and AI platforms those solutions run on. That taught me what works, and what doesn't, when a company has to pull many systems into one trustworthy picture.
I started this practice because I kept seeing the same pattern: companies that needed senior data leadership for a few critical months, not a permanent hire or a long consulting engagement. I come in, build the foundation, and hand your team the keys.
M.S. Analytics (Data Science), Georgia Institute of Technology. B.S. Statistics & Applied Mathematics, Rose-Hulman Institute of Technology. Based in Milwaukee.
I've spent 8+ years in supply chain and operations analytics at a multi-site medical device manufacturer. I started as a data scientist building predictive models for inventory and demand planning, then moved into platform ownership: deciding what a company's data and AI stack should be, and building it. I've led platform strategy, made the build-or-buy calls, architected production Snowflake environments, and shipped the infrastructure as code, ML pipelines, and AI agents that run on them.
That work taught me what most growing mid-market companies learn the hard way: the analytics gap is an architecture problem, not a tooling problem. Every acquisition brings another ERP, another customer list, another set of item codes. Getting from there to governed, trustworthy numbers takes a series of decisions best made by someone who has made them before. I build every foundation to the standard regulated industries require, whether or not you're in one.
I started this practice because I kept seeing the same pattern: companies that needed a senior data engineering lead for a few critical months, not a permanent hire or a long consulting engagement. I come in with an opinionated reference stack and reusable infrastructure as code, build the foundation, and hand your team the keys.
M.S. Analytics (Data Science), Georgia Institute of Technology. B.S. Statistics & Applied Mathematics, Rose-Hulman Institute of Technology. Based in Milwaukee.